Microbial polyhydroxyalkanoate synthesis from field pea starch hydrolysate
Bibliographic record
Abstract
Significant growth is anticipated in the plant-based protein industry over the next five years. To ensure sector viability into the future, development of value-added applications for starchy by-products from pulse crops such as field peas is essential. This study demonstrates utilization of field pea starch — more crystalline than other starches — as an inexpensive carbon source for microbial poly(3-hydroxyalkanoate) (PHA) biopolymer production. Commercial enzymes typically used for cereal starches (Stargen and a cocktail of Stargen, Optimash, GC626) generated sugar hydrolysates with similar glucose content (~80 g L −1 ) from 10 % (w v −1 ) crude pea starch. When used as a carbon source for PHA biosynthesis in several strains, the Stargen hydrolysate supported comparable PHA synthesis characteristics as commercial glucose, with the strains Paraburkholderia sacchari and Burkholderia thailandensis performing best. During cultivation on 15 g L −1 glucose-equivalent concentration of the Stargen hydrolysate, the intracellular PHA content reached up to 48 % of the dry biomass and the PHA titer was around 2 g L −1 in shake flasks. Despite having higher protein content, the triple-enzyme hydrolysate yielded no obvious benefit compared to the Stargen treatment for growth or PHA synthesis. The results suggest that Stargen alone can effectively hydrolyze crude field pea starch, and the resulting hydrolysate is suitable for production of PHA biopolymers. To our knowledge, this is the first study producing PHA from field pea starch hydrolysates in submerged cultivation, highlighting a promising co-product strategy to support a sustainable and resilient plant protein sector.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".